Yang Yang, Zhang Wenming, Chen Shengbin, Wang Xiaogang, Xia Yuangu, Liu Ji, Hu Bin, Lu Qiang, Zhang Bing
School of New Energy, North China Electric Power University, Beijing 102206, People's Republic of China.
National Engineering Research Center of New Energy Power Generation, North China Electric Power University, Beijing 102206, People's Republic of China.
ACS Omega. 2024 Jan 8;9(3):3392-3400. doi: 10.1021/acsomega.3c06689. eCollection 2024 Jan 23.
Zeolites are a very important family of catalysts. The catalytic activity of zeolites depends on the distribution of acid sites, which has been extensively studied. However, the relationship between the acid site distribution and catalytic efficiency remains unestablished. An onerous computational burden can be imposed when static calculations are applied to analyze the relationship between a catalyst structure and its energy. To resolve this issue, the current work uses neural network (NN) models to evaluate the relationship. By taking the typical HZSM-5 zeolite as an example, we applied the provided atomic coordinates to predict the energy. The network performances of the artificial neural network (ANN) and high-dimensional neural network (HDNN) are compared using the trained results from a dataset containing the identical number of acid sites. Furthermore, the importance of the feature is examined with the aid of a random forest model to identify the pivotal variables influencing the energy. In addition, the HDNN is employed to forecast the energy of an HZSM-5 system with varying numbers of acid sites. This study emphasizes that the energy of zeolites can be rapidly and accurately predicted through the NN, which can assist our understanding of the relationship between the structure and properties, thereby providing more accurate and efficient methods for the application of zeolite materials.
沸石是一类非常重要的催化剂。沸石的催化活性取决于酸位点的分布，对此已进行了广泛研究。然而，酸位点分布与催化效率之间的关系尚未确立。当应用静态计算来分析催化剂结构与其能量之间的关系时，可能会带来繁重的计算负担。为解决这一问题，当前工作使用神经网络（NN）模型来评估这种关系。以典型的HZSM - 5沸石为例，我们应用所提供的原子坐标来预测能量。使用来自包含相同数量酸位点的数据集的训练结果，比较了人工神经网络（ANN）和高维神经网络（HDNN）的网络性能。此外，借助随机森林模型检查特征的重要性，以识别影响能量的关键变量。另外，使用HDNN预测具有不同数量酸位点的HZSM - 5系统的能量。本研究强调，通过神经网络可以快速准确地预测沸石的能量，这有助于我们理解结构与性能之间的关系，从而为沸石材料的应用提供更准确、高效的方法。
